The COVID-19 pandemic has dramatically accelerated the growth and adoption of technology in the business world, particularly artificial intelligence (AI). While the presence of the deadly virus has largely receded from public discourse, it has left lasting impacts on how businesses operate. New work arrangements and tools that support the same have now become mainstream. Thus, it is good to know what the new year (2024) will have in store for the tech business world.
This article looks at the top AI trends in 2024 and how they will impact the business community.
Top 10 AI Trends in 2024
According to a report by McKinsey, companies are rapidly adopting artificial intelligence, with 56% of them already using it for at least one task. AI helps businesses reduce costs, streamline and automate processes, and increase efficiency and profitability.
According to a publication by the International Data Corporation (IDC), the artificial intelligence industry, which includes hardware, software, and services, generated nearly $430 billion in revenue in 2022. Experts believe the market will break the $500 billion barrier in 2024.
Here are the hot AI trends you should look at in 2024.
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The Rise of Quantum Machine Learning
Quantum computing is the next big thing in the technology world. As an emerging technology, it aspires to solve problems that are too complex for classical computing, including supercomputers. To achieve this, it uses the principles of quantum mechanics to solve intricate and challenging issues.
Companies like IBM, Microsoft, and Amazon are heavily investing in this technology and offering quantum computing tools and simulators. Quantum computing can be used to develop incredible machine-learning models, thus enabling you to overcome issues that currently seem unsolvable.
The primary goal of quantum machine learning (a developing field) is to expedite problem-solving by applying the principles of quantum mechanics and computing to machine learning. It solves complex problems with numerous variables that interact with each other in very complicated ways.
Hence, it is a rapidly growing field that will significantly impact how companies resolve internal issues and offer innovative solutions.
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Increasing Use of Artificial Intelligence in Process Discovery
Process smart data discovery, also known as Business Process Discovery (BPD), refers to the method used to comprehend and catalog all the aspects of a business process. In other words, it is the process whereby a business discovers how processes are executed in the organization. This allows them to identify the ones where automation will be most beneficial.
AI-based models are poised to play an ever-greater role in process discovery, thus helping companies automate, increase efficiency, and eventually profitability.
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Automated Machine Learning
Automated machine learning is the process of automating the tedious and arduous process of developing machine learning models. It allows developers and data scientists to build high-quality, scalable, efficient ML models.
Automated machine learning will involve advanced tools for data labeling and automated modification of neural network structures. Artificial intelligence significantly reduces the time and money to market new solutions by automating the process of choosing and improving neural network models.
Therefore, artificial intelligence and machine learning companies are likely to make significant investments in AutoML to reap the maximum benefits of fast ML model development time.
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Advancements in Predictive Analytics
Predictive analytics is rapidly expanding into numerous industries, and advancements in the field are indeed something to look forward to. It employs historical data, machine learning algorithms, and statistics to predict the future.
But the field itself is not new. It’s only in recent years that predictive analytics has become a means for business analysts to make better-informed decisions based on historical data.
As companies vie for greater competitiveness, predictive analytics will continue to grow in importance.
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The Emergence of Hyperautomation
Hyperautomation refers to automating processes beyond traditional business processes that are usually automated. Gartner defines it as the “business-driven, disciplined approach” employed by companies to automate their operations to the maximum level.
It uses multiple tools to achieve this, including artificial intelligence, RPA (robotic process automation), EPA (event-driven architecture), machine learning, packaged software, etc. Moreover, it allows you to discover business processes needing automation, followed by bot creation that enables them.
Hyperautomation will continue to guide companies’ digital transformation journeys worldwide. It seamlessly synergizes with artificial intelligence, machine learning, and RPA to automate.
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Increasing Use of AIOps
Artificial Intelligence for IT Operations, or AIOps (Artificial Intelligence Operations), is a term coined by Gartner in 2016. It refers to the combination of machine learning and big data to automate IT operations processes. AIOps helps identify event correlation, causality determination, and anomaly detection.
As IT operations become increasingly complex, companies will look to AIOps solutions to streamline their business processes, make critical business decisions, and enhance their activities by analyzing big data.
Companies that enable cross-team cooperation, offer visibility across multiple domains and integrate IT operations management will offer the best AIOps solutions.
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AI in Healthcare
Artificial intelligence has disrupted the healthcare system. Hospitals, clinics, care providers, and pharmaceutical companies generate vast amounts of data. Thanks to AI’s incredible benefits, all this data can now be processed and analyzed to drive business growth.
Humans cannot process such huge amounts of data, but AI-powered tools and solutions can—and they are already doing it. AI in healthcare offers accurate diagnosis, helps track disease progression, automates clinical workflows, and allows doctors to prescribe better treatments.
Moreover, telemedicine is a new field that is gaining popularity because patients suffering from mental health issues prefer AI enabled ERP chatbots. The reason is the feeling that one is not being judged.
One can say with certainty that the future of healthcare is closely linked to the use of AI, machine learning, and other emerging technologies.
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AI in the Manufacturing Sector
The manufacturing sector is quickly adopting AI models for multiple reasons. One of the significant aims is to enable improved quality assurance and workflow automation. Production automation, for example, automatically tracks goods production and helps you deliver high-quality products.
Similarly, automobile manufacturers are rapidly adopting AI-enabled automated assembling tools that can identify parts’ defects while moving along the assembly line. In the future, companies will employ AI for automatic defect detection and related decision-making.
The manufacturing industry is using AI trends and machine learning in various ways. This trend will only continue and strengthen in the coming years.
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AI with Internet of Things and Digital Twins
Businesses worldwide are increasingly shifting to new technologies like IoT (Internet of Things) and Digital Twins to enhance their capabilities.
Take IoT as an example. Self-driving cars, also known as autonomous cars, are no longer in their conceptual stages. Companies like Uber are testing these cars with IoT sensors to revolutionize the transportation industry. Moreover, smartphones are now ubiquitous, and with their widespread use, the rise of smart devices linked to these phones is also increasing.
Similarly, digital twins (virtual replicas of real-world entities) are also becoming very popular, especially in large-scale manufacturing, the energy sector, and urban development. The industry is expected to reach the $50 billion mark in 2026.
AI trends are closely associated with these and other such technologies. Hence the trend is set to rise in the future.
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AI and Cybersecurity
Today, cybersecurity is one of the leading concerns of not only enterprises but also governments and individuals. Everyone wants to protect their precious data and digital assets.
Artificial intelligence is critical in helping organizations fend off cybersecurity threats. It extends beyond security automation, enabling data security, helping identify potential threats, and uncovering system loopholes and anomalies.
However, the uses of artificial intelligence are cut both ways as hackers and cybercriminals use the same technology to outsmart your cyber defenses. Nevertheless, your company and its digital assets will be at greater risk if you are not using artificial intelligence to bolster your cybersecurity.
Wrap Up
In this article, we have merely touched the tip of the iceberg. In reality, the use and adoption of artificial intelligence are increasing at a lightning pace. From accessing customer insights and crunching big data to automating production processes and enhancing cybersecurity, artificial intelligence and its associated technologies like machine learning and deep learning are gaining massive traction.
All this also entails ethical dilemmas, regulatory loopholes, threats to law and order, and world peace. However, just because man can use technology for nefarious purposes does not mean it is inherently evil.
As explained in this article, the use of AI for positive purposes is growing exponentially and will continue to grow. Innovative, intelligent, and sustainable AI solutions have great potential for achieving great things.
Are you looking to build AI trends and machine learning models for your business? Contact us at [email protected] to get in touch with our artificial intelligence experts – we would love to see what we can do for you.